The present invention is directed to systems, apparatus and methods for the discovery of content within a social network, and more specifically, to a system to enable members of a social network to discover music of interest contained in the music libraries of network members. The system utilizes an automated process to calculate similarity and/or compatibility measures based on members' music listening habits and music libraries, and combines the measures with filtering and other techniques to produce a set of music discovery tools for network members.
The advent and popularity of portable music players, for example, MP3 players, has provided users with the ability to access a large number of songs in a convenient manner. The portability and relatively large storage capacity of such devices has made it possible for users to have their entire music collection on a single, transportable device. At the same time it has created great interest in developing ways for users to learn of (i.e., discover) music that might be of interest to them so that they can add the music to their collection. Traditionally, the primary ways for a user to discover music have been via (1) the radio, and (2) word of mouth (i.e., via friends). However, in addition to such methods, there has been recent interest in developing other ways for users to learn of music that might be of interest to them.
In an effort to assist users to discover music that might be of interest to them, several approaches have been pursued. These approaches generally rely on a user publishing a list of their music interests or current music being enjoyed (e.g., a playlist) and then enabling friends or other invitees to access the list. One example of such a playlist publishing model involves allowing a user to publish a playlist to a web site, and then enabling the list to be accessed by specific friends or acquaintances. Those accessing the list may review the playlist and in some cases, be provided with a link to enable download of selected songs from the list. Apple Computer™, the creator of iTunes™, provides a feature named iMix which permits a user to publish a playlist from an iTunes™ media library. The list can be sent to a friend, who may then browse the playlist and purchase & download individual songs from the list or purchase the entire playlist. Other similar offerings include those of WebJay™ (recently acquired by Yahoo™) which provides a website for sharing playlists, and Musicmobs (www.musicmobs.com; which assists a user to upload a playlist from iTunes™ to the Musicmobs website). However, all of these approaches require that a user actively publish a playlist to a web site or web-service, and then optionally invite one or more friends via a message to access the published playlist. When the friend accesses the published playlist, the friend must then decide whether or not they like the music, and if so, decide whether to purchase one or more of the songs.
Another approach to assisting users to discover music that might be of interest is allowing a user to publish their personal musical tastes to a location accessible by others. The leading companies in this space are believed to include Last.FM™ and Musicmobs. Both companies are believed to provide a user with client-side software that automatically examines the user's media library, tracks what music is being played, and uploads that data to a website. It is understood that the uploaded data is processed to report certain of the user's music listening habits (e.g., most-listened-to-artists or most-listened-to-songs) for others to access. Such services may also allow a user to publish their music listening habits (as determined by these web services) as an embedded resource on another website (such as a web page on a social network, e.g., MySpace™).
Although the above described approaches to music discovery do permit a user to share their music interests and listening habits with others, they do not provide a complete solution to the problem. The above approaches generally lack sufficient automation or other data processing assistance that can add value by assisting those viewing the data to determine which music would be of greatest interest. This is because the present approaches operate so that once a playlist or summary of listening habits is published, those accessing the information have to decide for themselves (with no further information) whether the listed songs are of interest. Since users may have a music collection numbering in the thousands of songs, without more information, it is very difficult for someone to select which, if any, of a friend's music is of sufficient interest to warrant listening to or downloading. In this sense, present methods lack a way to assess the likelihood that music of interest to one user will be of interest to another. Further, present methods lack a way to quantify this likelihood, should that be of interest to a user.
What is desired is a system, apparatus and method for enabling the efficient discovery of and access to music content, where such system, apparatus, and method overcomes the noted disadvantages of present approaches.